See my lab output here

I. Reading and processing the NYT state-level COVID-19 data.

1. Read in the data

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

cv_states <- merge(cv_states, state_pops, by= "state")

2. Look at the data

  • Inspect the dimensions, head, and tail of the data
  • Inspect the structure of each variables. Are they in the correct format?
dim(cv_states)
## [1] 32250     9
head(cv_states)
##     state       date fips  cases deaths geo_id population pop_density abb
## 1 Alabama 2020-06-10    1  21989    744      1    4887871    96.50939  AL
## 2 Alabama 2021-10-31    1 832047  15573      1    4887871    96.50939  AL
## 3 Alabama 2021-05-26    1 542831  11138      1    4887871    96.50939  AL
## 4 Alabama 2020-04-19    1   4903    160      1    4887871    96.50939  AL
## 5 Alabama 2021-07-07    1 552911  11387      1    4887871    96.50939  AL
## 6 Alabama 2020-06-21    1  30021    839      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips cases deaths geo_id population pop_density abb
## 32245 Wyoming 2020-12-01   56 33805    230     56     577737    5.950611  WY
## 32246 Wyoming 2021-08-15   56 68272    793     56     577737    5.950611  WY
## 32247 Wyoming 2021-03-16   56 55352    693     56     577737    5.950611  WY
## 32248 Wyoming 2021-04-12   56 56988    701     56     577737    5.950611  WY
## 32249 Wyoming 2021-04-01   56 56389    700     56     577737    5.950611  WY
## 32250 Wyoming 2020-11-30   56 33305    215     56     577737    5.950611  WY
str(cv_states)
## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2020-06-10" "2021-10-31" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  21989 832047 542831 4903 552911 30021 117242 809485 134417 547135 ...
##  $ deaths     : int  744 15573 11138 160 11387 839 2037 14869 2285 11252 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

The variables are all in the correct format.

3. Format the data

  • Make date into a date variable, by year-month-day
  • Make state into a factor variable (what is that?)
  • Order the data first by state, second by date
  • Confirm the variables are now correctly formatted
  • Inspect the range values for each variable. What is the date range? The range of cases and deaths?
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list) # what is this?
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips  cases deaths geo_id population pop_density abb
## 31735 Wyoming 2021-11-06   56 105318   1243     56     577737    5.950611  WY
## 31791 Wyoming 2021-11-07   56 105318   1243     56     577737    5.950611  WY
## 31967 Wyoming 2021-11-08   56 105990   1243     56     577737    5.950611  WY
## 31942 Wyoming 2021-11-09   56 106287   1298     56     577737    5.950611  WY
## 31961 Wyoming 2021-11-10   56 106698   1298     56     577737    5.950611  WY
## 32016 Wyoming 2021-11-11   56 106698   1298     56     577737    5.950611  WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
summary(cv_states)
##            state            date                 fips           cases        
##  Washington   :  661   Min.   :2020-01-21   Min.   : 1.00   Min.   :      1  
##  Illinois     :  658   1st Qu.:2020-08-03   1st Qu.:16.00   1st Qu.:  31774  
##  California   :  657   Median :2021-01-05   Median :29.00   Median : 146459  
##  Arizona      :  656   Mean   :2021-01-05   Mean   :29.78   Mean   : 386609  
##  Massachusetts:  650   3rd Qu.:2021-06-09   3rd Qu.:44.00   3rd Qu.: 481786  
##  Wisconsin    :  646   Max.   :2021-11-11   Max.   :72.00   Max.   :4993930  
##  (Other)      :28322                                                         
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.:  621   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 2658   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   : 7155   Mean   :29.78   Mean   : 6433897   Mean   :  422.513  
##  3rd Qu.: 8432   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :73132   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :609        
##       abb       
##  WA     :  661  
##  IL     :  658  
##  CA     :  657  
##  AZ     :  656  
##  MA     :  650  
##  WI     :  646  
##  (Other):28322
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2021-11-11"

4. Add new_cases and new_deaths and correct outliers

  • Add variables for new cases, new_cases, and new deaths, new_deaths
    • Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
  • Filter to dates after June 1, 2021
  • Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
  • Correct outliers: Set negative values for new_cases or new_deaths to 0
  • Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
  • Get the rolling average of new cases and new deaths to smooth over time
  • Inspect data again interactively
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i]) # subset for just one state
  cv_subset = cv_subset[order(cv_subset$date),] # order that state by date
  
  # add starting level for new cases and deaths 
  cv_subset$new_cases = cv_subset$cases[1] 
  cv_subset$new_deaths = cv_subset$deaths[1] 
  
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] 
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases 
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")

# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = 0.5, alpha = 0.5)
#ggplotly(p1)
p1<-NULL # to clear from work space
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = 0.5, alpha = 0.5)
#ggplotly(p2)
p2<-NULL # to clear from work space

# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0

# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  
  # include in main data set
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Use ggplotly to inspect smoothened new_deaths data for each state
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2=NULL

5. Add additional variables

  • Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

    • per100k = cases per 100,000 population
    • newper100k= new cases per 100,000
    • deathsper100k = deaths per 100,000
    • newdeathsper100k = new deaths per 100,000

  • Add a “naive CFR” variable representing deaths / cases on each date for each state

  • Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using plot_ly()

  • Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
    • Color points by state and size points by state population
    • Use hover to identify any outliers.
    • Remove those outliers and re-plot.
  • Choose one plot. For this plot:
    • Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similar to how we did this in the lecture notes
    • Add layout information to title the chart and the axes
    • Enable hovermode = "compare"
# pop_density vs. cases
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 25), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")

# pop_density vs. cases after filtering
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 25), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 25), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 25), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), 
                         paste(" Cases per 100k: ", per100k, sep=""), 
                         paste(" Deaths per 100k: ", deathsper100k, sep=""), 
                         sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

  • Use ggplotly to explore pop_density vs. newdeathsper100k
  • Explore the pattern between \(x\) and \(y\) using geom_smooth()
    • Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?
# 
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=newdeathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)

There does not appear to be a strong correlation between population density and new deaths, at least when looking at the most recent day’s worth of data.

8. Multiple line chart

  • Create a line chart of the naive_CFR for all states over time using plot_ly()
    • Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?
  • Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()
    • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_trace(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns. - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

# Map state, date, and new_cases to a matrix
library(tidyr)

cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2) #heatmap requires a matrix input

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks") # a list of the dates of interest
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

  • Create a map to visualize the naive_CFR by state on October 15, 2021
  • Compare with a map visualizing the naive_CFR by state on most recent date
  • Plot the two maps together using subplot(). Make sure the shading is for the same range of values
  • Describe the difference in the pattern of the CFR
### For specified date
pick.date = "2021-10-15"

# Extract relevant data for each state, clean up variable names
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig

#############
### Map for today's date
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig

### Plot together 
subplot(
  fig_pick.date,
  fig_Today, 
  nrows = 2, margin = .05)